Szczegóły publikacji
Opis bibliograficzny
Deep learning algorithm for differentiating patients with a healthy liver from patients with liver lesions based on MR images / Maciej Skwirczyński, Zbisław TABOR, Julia LASEK, Zofia SCHNEIDER, Sebastian Gibała, Iwona Kucybała, Andrzej Urbanik, Rafał Obuchowicz // Cancers [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2072-6694. — 2023 — vol. 15 iss. 12 art. no. 3142, s. 1–15. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 13–15, Abstr. — Publikacja dostępna online od: 2023-06-11
Autorzy (8)
- Skwirczyński Maciej
- AGHTabor Zbisław
- AGHLasek Julia
- AGHSchneider Zofia
- Gibała Sebastian
- Kucybała Iwona
- Urbanik Andrzej
- Obuchowicz Rafał
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 148823 |
|---|---|
| Data dodania do BaDAP | 2023-09-26 |
| Tekst źródłowy | URL |
| DOI | 10.3390/cancers15123142 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Cancers |
Abstract
The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a p-value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a p-value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver.
